class_docstr: Class for performing sampling in Continuous model
  using Hamiltonian Monte Carlo with dual averaging for
  adaptaion of parameter stepsize.
  
  Parameters:
  -----------
  model: Model from which sampling has to be done
  
  simulate_dynamics: Class to propose future states of position
    and momentum in time by simulating HamiltonianDynamics
  
  delta: float (in between 0 and 1), defaults to 0.65
    The target HMC acceptance probability
  
  References
  -----------
  Matthew D. Hoffman, Andrew Gelman, The No-U-Turn Sampler: Adaptively
  Setting Path Lengths in Hamiltonian Monte Carlo. Journal of
  Machine Learning Research 15 (2014) 1351-1381
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code: def __init__(self, model, simulate_dynamics=LeapFrog, delta=0.65):
      if not isinstance(delta, float) or delta > 1.0 or delta < 0.0:
          raise ValueError(
              "delta should be a floating value in between 0 and 1"
          )
  
      self.delta = delta
  
      super(HamiltonianMCDA, self).__init__(
          model=model, simulate_dynamics=simulate_dynamics
      )
  
file_path: dragonfly/dragonfly/sampling/hmc.py
imports: from __future__ import division
  from math import sqrt
  import numpy
  from .check_functions import _check_1d_array_object
  from .base import LeapFrog, BaseSimulateHamiltonianDynamics
label_desc: Use image node_id 1 to create a new HamiltonianMCDA object from inherited base classes: HamiltonianMC with example: obj = HamiltonianMCDA(model, simulate_dynamics, delta)